Algorithmic Bias and Policy: Understanding Fairness in AI

As automated systems increasingly decide who gets a loan, who gets hired, and how public resources are allocated, understanding the ethical implications of technology is more critical than ever. This text-based course guides you through the foundational intersection of technology, ethics, and governance. You will learn to identify where algorithmic bias originates, how it impacts diverse communities, and how modern policy frameworks aim to regulate these powerful tools. What you'll learn: • Understand the core terminology of machine learning, data collection, and how bias enters automated systems • Analyze real-world policy case studies in criminal justice, finance, and healthcare • Explore modern regulatory approaches, including international AI governance frameworks and ethical guidelines • Evaluate the social and ethical implications of generative AI models and automated decision-making • Practice identifying systemic bias in data models through written analysis exercises • Develop policy-oriented strategies to advocate for algorithmic transparency and fairness. You will begin with foundational definitions of algorithms and bias before progressing to real-world policy responses and modern governance frameworks. Through structured readings and written analytical exercises, you will build a comprehensive toolkit for evaluating technology policies. This course is designed for beginners, policymakers, advocates, and tech enthusiasts, with no prior programming or technical background required. Start reading today to help shape a fairer, more transparent algorithmic future.